1. Field of the Invention
The present invention relates to an image processing apparatus that processes time-series images that are captured in time series, an image processing program recording medium, and an image processing method.
2. Description of the Related Art
In recent years, medical apparatuses as represented by capsule endoscopes that sequentially capture time-series in-vivo images of a lumen (digestive tract) while moving through the lumen have been developed, which are apparatuses that examine a moving object to be examined from time-series images of the object to be examined that are captured in time series. A capsule endoscope is swallowed and then conveyed through the lumen, for example, due to peristalsis. The capsule endoscope sequentially captures images at a predetermined imaging rate and transmits the images to an external receiving device, and it is eventually excreted to the outside of the body. The number of captured time-series images is roughly represented by the imaging rate (about 2 to 4 frames/sec)×time for which the capsule endoscope stays in the body (about 8 hours=8×60×60 sec), which amounts to more than a few tens of thousands. Doctors spend much time on checking a large number of time-series images, which are recorded in the external receiving device, using a diagnostic work station in order to identify a lesion; therefore, technologies for facilitating operations of doctors to check the images are strongly demanded.
For example, Japanese Laid-open Patent Publication No. 2007-175432 discloses a method of detecting a lesion, which is an abnormal site, from time-series images that are captured by a capsule endoscope. Specifically, images that are sampled at predetermined intervals from time-series images are each divided into rectangular areas. After the rectangular areas are projected in a predetermined feature space, such as an average color ratio space, the rectangular areas are clustered. The resulting clusters are classified into classes of mucosa, feces (contents), and bubbles using a classifier that is prepared beforehand based on teaching data. Thereafter, the sampled images are divided into sub-sets at predetermined chronological intervals, and the frequency of occurrence and a distribution of feature data of each of the classes in each subset are calculated from the result of the classification into the classes. According to such information, the rectangular areas of the images in the chronological interval represented by each sub-set are classified into the classes. By comparing the feature data of each rectangle area, which is classified as mucosa in the image as a result of the classification, with average feature data of all the rectangular areas, which are classified as mucosa in the image, a lesion is detected.